1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
|
/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <cfloat>
#include "caffe2/core/context_gpu.h"
#include "modules/detectron/sigmoid_focal_loss_op.h"
namespace caffe2 {
namespace {
__global__ void SigmoidFocalLossKernel(
const int N, const int D, const int H, const int W, const float* logits,
const int* targets, const float* weight_pos,
const float gamma, const float alpha,
const int num_classes, float* losses) {
CUDA_1D_KERNEL_LOOP(i, N * D * H * W) {
int x = i % W;
int y = (i / W) % H;
int c = (i / (W * H)) % D; // channel, here D is channel dim in input NxDxHxW
int n = i / (W * H * D); // n in NxDxHxW
int A = D / num_classes; // num_anchors = A
int a = c / num_classes; // current anchor out of A anchors in D = A * num_cls
int d = c % num_classes; // current class
int t = targets[n * (H * W * A) + a * (H * W) + y * W + x]; // target
// check whether the class is true class or not.
// The target classes are in range 1 - 81 and the d is in range 0-80
// because we predict A*80 dim, so for comparison purpose, compare t and (d+1)
float c1 = (t == (d + 1));
float c2 = (t != -1 & t != (d + 1));
float Np = c10::cuda::compat::max(weight_pos[0], static_cast<float>(1.0));
float zn = (1.0 - alpha) / Np;
float zp = alpha / Np;
// p = 1. / 1. + expf(-x)
float p = 1. / (1. + expf(-logits[i]));
// (1 - p)**gamma * log(p) where
float term1 = powf((1. - p), gamma) * logf(c10::cuda::compat::max(p, FLT_MIN));
// p**gamma * log(1 - p)
float term2 =
powf(p, gamma) *
(-1. * logits[i] * (logits[i] >= 0) -
logf(1. + expf(logits[i] - 2. * logits[i] * (logits[i] >= 0))));
losses[i] = 0.0;
losses[i] += -c1 * term1 * zp;
losses[i] += -c2 * term2 * zn;
}
}
__global__ void SigmoidFocalLossGradientKernel(
const int N, const int D, const int H, const int W, const float* logits,
const int* targets, float* dX_data, const float* weight_pos,
const float gamma, const float alpha, const int num_classes,
const float* avg_loss) {
CUDA_1D_KERNEL_LOOP(i, N * D * H * W) {
float a_loss = avg_loss[0];
int x = i % W;
int y = (i / W) % H;
int c = (i / (W * H)) % D;
int n = i / (W * H * D);
int A = D / num_classes; // num_anchors
int a = c / num_classes; // current anchor
int d = c % num_classes; // current class
float Np = c10::cuda::compat::max(weight_pos[0], static_cast<float>(1.0));
float zn = (1.0 - alpha) / Np;
float zp = alpha / Np;
int t = targets[n * (H * W * A) + a * (H * W) + y * W + x];
float c1 = (t == (d + 1));
float c2 = (t != -1 & t != (d + 1));
float p = 1. / (1. + expf(-logits[i]));
// (1-p)**g * (1 - p - g*p*log(p))
float term1 =
powf((1. - p), gamma) *
(1. - p - (p * gamma * logf(c10::cuda::compat::max(p, FLT_MIN))));
// (p**g) * (g*(1-p)*log(1-p) - p)
float term2 =
powf(p, gamma) *
((-1. * logits[i] * (logits[i] >= 0) -
logf(1. + expf(logits[i] - 2. * logits[i] * (logits[i] >= 0)))) *
(1. - p) * gamma - p);
dX_data[i] = 0.0;
dX_data[i] += -c1 * zp * term1;
dX_data[i] += -c2 * zn * term2;
dX_data[i] = dX_data[i] * a_loss;
}
}
} // namespace
template<>
bool SigmoidFocalLossOp<float, CUDAContext>::RunOnDevice() {
// Input logits, for example: N x (A * 80) x H x W in cls-agnostic
auto& X = Input(0);
// Target, for example: N x A x H x W
auto& T = Input(1);
// Number of positive examples: scalar
auto& wp = Input(2);
// output avg Sigmoid focal loss as mentioned in RetinaNet paper
int N = X.dim32(0);
int D = X.dim32(1);
int H = X.dim32(2);
int W = X.dim32(3);
auto* avg_loss = Output(0, vector<int64_t>(), at::dtype<float>());
losses_.ResizeLike(X);
float* avg_loss_data = avg_loss->mutable_data<float>();
SigmoidFocalLossKernel<<<CAFFE_GET_BLOCKS(X.size()),
CAFFE_CUDA_NUM_THREADS, 0, context_.cuda_stream()>>>(
N, D, H, W, X.data<float>(), T.data<int>(),
wp.data<float>(), gamma_, alpha_, num_classes_,
losses_.mutable_data<float>());
C10_CUDA_KERNEL_LAUNCH_CHECK();
math::Sum<float, CUDAContext>(
losses_.size(), losses_.data<float>(), avg_loss_data, &context_);
math::Scale<float, float, CUDAContext>(
1, scale_, avg_loss_data, avg_loss_data, &context_);
return true;
}
template<>
bool SigmoidFocalLossGradientOp<float, CUDAContext>::RunOnDevice() {
auto& X = Input(0);
auto& T = Input(1);
auto& wp = Input(2);
auto& d_avg_loss = Input(InputSize() - 1);
// get input shape
int N = X.dim32(0);
int D = X.dim32(1);
int H = X.dim32(2);
int W = X.dim32(3);
auto* dX = Output(0, X.sizes(), at::dtype<float>());
SigmoidFocalLossGradientKernel<<<CAFFE_GET_BLOCKS(X.size()),
CAFFE_CUDA_NUM_THREADS, 0, context_.cuda_stream()>>>(
N, D, H, W, X.data<float>(), T.data<int>(), dX->mutable_data<float>(),
wp.data<float>(), gamma_, alpha_, num_classes_,
d_avg_loss.data<float>());
C10_CUDA_KERNEL_LAUNCH_CHECK();
math::Scale<float, float, CUDAContext>(
dX->size(),
scale_,
dX->data<float>(),
dX->mutable_data<float>(),
&context_);
return true;
}
REGISTER_CUDA_OPERATOR(SigmoidFocalLoss,
SigmoidFocalLossOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(SigmoidFocalLossGradient,
SigmoidFocalLossGradientOp<float, CUDAContext>);
} // namespace caffe2
|
